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Cited by 366

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

Immunohistochemical distribution and gene expression of transforming growth factor alpha in ovarian tissue of rats treated with capsaicin in puberty

YILDIZ, SEVDA ELİŞ, NAZLI, MÜMTAZ, NUR, GÖKHAN·2013·Turkish Journal of Medical Sciences

Abstract

The existence of a trend in a hydrological time series is detected by statistical tests. The power of a test is the probability that it will reject a null hypothesis when it is false. In this study, the power of the parametric t-test for trend detection is estimated by Monte Carlo simulation for various probability distributions and compared with the power of the non-parametric Mann-Kendall test. The t-test has less power than the non-parametric test when the probability distribution is skewed. However, for moderately skewed distributions the power ratio is close to one. Annual streamflow records in various regions of Turkey are analyzed by the two tests to compare their powers in detecting a trend.

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Metadata

Authors
YILDIZ, S. E., NAZLI, M., & NUR, G.
Venue
Turkish Journal of Medical Sciences
Published
2013-01-01
Volume
27
Issue
4
Pages
247-251
ISSN
1303-6165
OpenAlex
W803413940
Cited by
366
References
4
FWCI
1.49
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Immunohistochemical distribution and gene expression of transforming growth factor alpha in ovarian tissue of rats treated with capsaicin in puberty. https://app.cochat.ai/papers/10.3906/sag-1205-120
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YILDIZ, S. E., NAZLI, M., & NUR, G. (2013). Immunohistochemical distribution and gene expression of transforming growth factor alpha in ovarian tissue of rats treated with capsaicin in puberty. Turkish Journal of Medical Sciences. https://doi.org/10.3906/sag-1205-120

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.